ArticleMetabolites2024
metabCombiner 2.0: Disparate Multi-Dataset Feature Alignment for LC-MS Metabolomics.
Article in Metabolites, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Eclipse: a Python package for alignment of two or more nontargeted LC-MS metabolomics datasets.Bioinformatics (Oxford, England) · 2025Article
- Constructing a consensus serum metabolome.bioRxiv : the preprint server for biology · 2025Article
- Challenges in Lipidomics Biomarker Identification: Avoiding the Pitfalls and Improving Reproducibility.Metabolites · 2024Article
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11 authors.
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Abstract
Liquid chromatography-high-resolution mass spectrometry (LC-HRMS), as applied to untargeted metabolomics, enables the simultaneous detection of thousands of small molecules, generating complex datasets. Alignment is a crucial step in data processing pipelines, whereby LC-MS features derived from common ions are assembled into a unified matrix amenable to further analysis. Variability in the analytical factors that influence liquid chromatography separations complicates data alignment. This is prominent when aligning data acquired in different laboratories, generated using non-identical instruments, or between batches from large-scale studies. Previously, we developed metabCombiner for aligning disparately acquired LC-MS metabolomics datasets. Here, we report significant upgrades to metabCombiner that enable the stepwise alignment of multiple untargeted LC-MS metabolomics datasets, facilitating inter-laboratory reproducibility studies. To accomplish this, a "primary" feature list is used as a template for matching compounds in "target" feature lists. We demonstrate this workflow by aligning four lipidomics datasets from core laboratories generated using each institution's in-house LC-MS instrumentation and methods. We also introduce batchCombine, an application of the metabCombiner framework for aligning experiments composed of multiple batches. metabCombiner is available as an R package on Github and Bioconductor, along with a new online version implemented as an R Shiny App.
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Registered trials
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